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» Content-Based Image Retrieval by Relevance Feedback
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IDEAL
2005
Springer
14 years 1 months ago
Kernel Biased Discriminant Analysis Using Histogram Intersection Kernel for Content-Based Image Retrieval
It is known that no single descriptor is powerful enough to encompass all aspects of image content, i.e. each feature extraction method has its own view of the image content. A pos...
Lin Mei, Gerd Brunner, Lokesh Setia, Hans Burkhard...
MTA
2008
146views more  MTA 2008»
13 years 7 months ago
A survey of browsing models for content based image retrieval
The problem of content based image retrieval (CBIR) has traditionally been investigated within a framework that emphasises the explicit formulation of a query: users initiate an au...
Daniel Heesch
ICMCS
2000
IEEE
116views Multimedia» more  ICMCS 2000»
13 years 12 months ago
Non-linear Relevance Feedback: Improving the Performance of Content-Based Retrieval Systems
In this paper, a non-linear relevance feedback mechanism is proposed for increasing the performance and the reliability of content-based retrieval systems. In particular, the huma...
Nikolaos D. Doulamis, Anastasios D. Doulamis, Stef...
ICMCS
2000
IEEE
142views Multimedia» more  ICMCS 2000»
13 years 12 months ago
Incorporate Discriminant Analysis with EM Algorithm in Image Retrieval
One of the difficulties of Content-Based Image Retrieval (CBIR) is the gap between high-level concepts and low-level image features, e.g., color and texture. Relevance feedback wa...
Qi Tian, Ying Wu, Thomas S. Huang
ICDE
2006
IEEE
191views Database» more  ICDE 2006»
14 years 8 months ago
Query Decomposition: A Multiple Neighborhood Approach to Relevance Feedback Processing in Content-based Image Retrieval
Today's Content-Based Image Retrieval (CBIR) techniques are based on the "k-nearest neighbors" (kNN) model. They retrieve images from a single neighborhood using lo...
Kien A. Hua, Ning Yu, Danzhou Liu